Zinc finger-inspired peptide-metal-phenolic nanointerface enhances bone-implant integration under bacterial infection microenvironment through immune modulation and osteogenesis promotion
Bibliographic record
Abstract
Orthopedic and dental implantations under bacterial infection microenvironment face significant challenges in achieving high-quality bone-implant integration. Designing implant coatings that incorporate both immune defense and anti-inflammation is difficult in conventional single-functional coatings. We introduce a multifunctional nanointerface using a zinc finger-inspired peptide-metal-phenolic nanocoating, designed to enhance implant osseointegration under such conditions. Abaloparatide (ABL), a second-generation anabolic drug for treating osteoporosis, can be integrated into the design of a zinc-phenolic network constructed on the implant surface (ABL@ZnTA). Importantly, the phenolic-coordinated Zn2+ ions in ABL@ZnTA can act as zinc finger motif to co-stabilize the configuration of ABL through multiple molecular interactions, enabling high bioactivity, high loading capacity (1.36 times), and long-term release (>7 days) of ABL. Our results showed that ABL@ZnTA can modulate macrophage polarization from the pro-inflammatory M1 towards the anti-inflammatory M2 phenotype, promoting immune osteogenesis with increased OCN, ALP, and SOD 1 expression. Furthermore, the ABL@ZnTA significantly reduces inflammatory fibrous tissue encapsulation and enhances the long-term stability of the implants, indicated by enhanced binding strength (6 times) and functional connectivity (1.5−3 times) in the rat bone defect model infected by S. aureus. Overall, our research offers a nano-enabled synergistic strategy that balances infection defense and osteogenesis promotion in orthopedic and dental implantations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".